Collaborative filtering systems are well known, as are online community systems. Examples of the former include Amazon.com's recommendation technology and other similar systems such as eMusic.com's. Examples of the latter include Google Groups.
However, none of the existing solutions effectively leverages the fact that users of online recommendations systems and online community systems typically own their own computers, and have the opportunity to make the central processing units of those computers available for making such systems more useful and enjoyable.
In particular, the task of matching people with extremely similar tastes and interests becomes very computationally difficult as the number of people increases and as the complexity of the similarity measure increases. With hundreds of thousands or even millions of people such as are typically enrolled in major online services, limitations of server hardware resources constrain the system's ability to find the best matches between people based on taste and interest.
To the degree that such matches are made with real accuracy, “neighborhoods” of individuals with extremely similar interests may be formed that can be used for purposes of recommendation and community.
What is needed, then, is an effective way of leveraging the computers owned by end-users of a community and recommendation system for the purpose massively-distributed similarity searching.